Generative AI systems rarely fail loudly. A prompt tweak, a model upgrade or a refreshed retrieval index can quietly degrade answer quality, and classical unit tests will not catch it. A language model can give many differently worded answers that are all correct, and one confident answer that is wrong.
This course gives you a practical framework for evaluating, testing and monitoring LLM applications across their whole lifecycle, so that quality becomes something you measure and enforce, not something you spot-check by hand. It is a focused design course: you learn the patterns, decisions and trade-offs behind a working evaluation system, illustrated with worked examples, and leave with a blueprint you can apply to your own pipelines with whichever tools your team already uses.
What you will be able to do:
- Define quality bars across accuracy, faithfulness, safety, latency and cost, with thresholds a business owner can sign off.
- Build evaluation datasets from production logs (often called gold sets: curated test questions with verified answers), while avoiding contamination and leakage.
- Choose between reference-based, reference-free and rubric-based metrics based on the risk of each use case.
- Design regression tests that use repeated sampling and tolerance bands to handle non-deterministic output.
- Write LLM-as-a-judge prompts with anchored rubrics that reduce verbosity and position bias.
- Check whether an automated judge can be trusted, using chance-corrected agreement with human raters.
- Place blocking and advisory quality gates in a CI/CD pipeline.
- Monitor LLM applications in production with traffic sampling, guardrail telemetry and incident response.
